AWAPart: Adaptive Workload-Aware Partitioning of Knowledge Graphs

TL;DR

This paper introduces AWAPart, an adaptive partitioning method for large-scale knowledge graphs that dynamically adjusts to query workload changes, reducing processing time and communication costs in distributed systems.

Contribution

The paper presents a novel adaptive partitioning approach that responds to workload variations, improving query efficiency over static partitioning methods.

Findings

Improved query processing times after dynamic re-partitioning

Reduced communication costs due to optimized edge cuts

Effective adaptation to changing query workloads

Abstract

Large-scale knowledge graphs are increasingly common in many domains. Their large sizes often exceed the limits of systems storing the graphs in a centralized data store, especially if placed in main memory. To overcome this, large knowledge graphs need to be partitioned into multiple sub-graphs and placed in nodes in a distributed system. But querying these fragmented sub-graphs poses new challenges, such as increased communication costs, due to distributed joins involving cut edges. To combat these problems, a good partitioning should reduce the edge cuts while considering a given query workload. However, a partitioned graph needs to be continually re-partitioned to accommodate changes in the query workload and maintain a good average processing time. In this paper, an adaptive partitioning method for large-scale knowledge graphs is introduced, which adapts the partitioning in response to changes in the query workload. Our evaluation demonstrates that the performance of processing time for queries is improved after dynamically adapting the partitioning of knowledge graph triples.